ReviewInsights into imaging2020
Deep learning workflow in radiology: a primer.
Review in Insights into imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05566158 (Development and Validation of a CT-based Diagnostic Models Using Artificial Intelligence for Detection of Small Bowel Obstruction), which is not on this map. Cited by 67 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Development and Validation of a CT-based Diagnostic Models Using Artificial Intelligence for Detection of Small Bowel Obstruction
Who cites it
67 citing papers in PubMed.
- Developing an artificial intelligence tool for detecting fractures of child abuse: preliminary findings.European radiology · 2026Article
- Balancing accuracy and efficiency in lumbar spine image segmentation using multi-scale attention and residual learning.Scientific reports · 2026Article
- Artificial intelligence prognostication of liver disease using imaging.The British journal of radiology · 2026Review
- Artifact-robust Deep Learning-based Segmentation of 3D Phase-contrast MR Angiography: A Novel Data Augmentation Approach.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Article
- Toward Smarter Orthopedic Care: Classifying Plantar Footprints from RGB Images Using Vision Transformers and CNNs.Journal of imaging · 2025Article
- ESR Essentials: a step-by-step guide of segmentation for radiologists-practice recommendations by the European Society of Medical Imaging Informatics.European radiology · 2025Review
- Characteristics and Distribution of Radiologists in Saudi Arabia: A Cross-Sectional Study Based on National Data.Healthcare (Basel, Switzerland) · 2025Article
- High-resolution conditional MR image synthesis through the PACGAN framework.Scientific reports · 2025Article
- AI Revolution in Radiology, Radiation Oncology and Nuclear Medicine: Transforming and Innovating the Radiological Sciences.Journal of medical imaging and radiation oncology · 2025Review
- Deep learning detects retropharyngeal edema on MRI in patients with acute neck infections.European radiology experimental · 2025Article
- Machine learning in dentistry and oral surgery: charting the course with bibliometric insights.Head & face medicine · 2025Article
- Automatic sequence identification in multicentric prostate multiparametric MRI datasets for clinical machine-learning.Insights into imaging · 2025Article
- Deep learning for identifying cervical ossification of the posterior longitudinal ligament: a systematic review and meta-analysis.Quantitative imaging in medicine and surgery · 2025Article
- Upskilling or deskilling? Measurable role of an AI-supported training for radiology residents: a lesson from the pandemic.Insights into imaging · 2025Article
- Automated CAD system for early detection and classification of pancreatic cancer using deep learning model.PloS one · 2025Article
- MRAnnotator: multi-anatomy and many-sequence MRI segmentation of 44 structures.Radiology advances · 2025Article
- Artificial intelligence: a primer for pediatric radiologists.Pediatric radiology · 2024Review
- Revolutionizing Brain Tumor Care: Emerging Technologies and Strategies.Biomedicines · 2024Review
- Enhancing Automated Brain Tumor Detection Accuracy Using Artificial Intelligence Approaches for Healthcare Environments.Bioengineering (Basel, Switzerland) · 2024Article
- Article
7 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Interest for deep learning in radiology has increased tremendously in the past decade due to the high achievable performance for various computer vision tasks such as detection, segmentation, classification, monitoring, and prediction. This article provides step-by-step practical guidance for conducting a project that involves deep learning in radiology, from defining specifications, to deployment and scaling. Specifically, the objectives of this article are to provide an overview of clinical use cases of deep learning, describe the composition of multi-disciplinary team, and summarize current approaches to patient, data, model, and hardware selection. Key ideas will be illustrated by examples from a prototypical project on imaging of colorectal liver metastasis. This article illustrates the workflow for liver lesion detection, segmentation, classification, monitoring, and prediction of tumor recurrence and patient survival. Challenges are discussed, including ethical considerations, cohorting, data collection, anonymization, and availability of expert annotations. The practical guidance may be adapted to any project that requires automated medical image analysis.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.